In an environment in which business tasks are to be crowdsourced, it's necessary to find a suitable crowdsourcing platform that meets the enterprise level requirements. The enterprise level requirements are represented in the form of a plurality of tasks which are diverse in nature and have varying requirements in terms of quality of work expected from crowdworkers in a crowdsourcing platform. The recent mushrooming of crowdsourcing platforms makes it difficult for enterprises looking to leverage the varied abilities of crowdworkers for fulfilling the requirements of the plurality of tasks. Recent experiments provide some valuable insight into how the quality of work expected from the crowdworkers varies from one crowdsourcing platform to another. However, since the crowdsourcing platforms are composed of a heterogeneous mix of the crowdworkers it becomes difficult for the enterprises to predict the variation and the quality of work that can be delivered by the crowdsourcing platform.
Some solutions offer static recommendations of the crowdsourcing platforms by predicting the quality of work based on human experience or aggregate summaries. There are solutions which circumvent this problem by trying to improve the quality after the plurality of tasks have already been assigned to crowdsourcing platforms by posting additional Human Intelligence Tasks (HITs) for the plurality of tasks, reposting the plurality of tasks, or reposting the plurality of tasks with added payments. There are other solutions which try to identify the skilled workers within crowdsourcing platforms for the requirements of the plurality of tasks in order to improve the quality of work. However, these solutions are unable to offer qualitative prediction of the quality of work for the requirements of the plurality of tasks. Also, these solutions increase the overheads due to un-informed assignment of the plurality of tasks. Furthermore, these solutions rely on human inputs which may be erroneous, outdated, or even manipulated.